Is Deforestation the Legacy of Transmigration?
Anna Lou Abatayo∗
Department of Food and Resource Economics
Center for Macroecology, Evolution and Climate
University of Copenhagen
May 30, 2016
Abstract
Although it is widely believed that the Indonesian Transmigration Program,
the world’s largest government-sponsored voluntary migration program, caused
significant deforestation in the outer islands of Indonesia, to date, there has
been no empirical evidence to support such claims. This paper examines both
the short- and long-run effects of transmigration on deforestation using archival
data, Landsat and MODIS satellite images, and a program evaluation approach. I find that an additional transmigrant family causes an initial 3.4 km2
of forest cover loss but has minimal impact in the long-run. The main mechanisms responsible for the increased deforestation appear to be the disruption
of local social cohesion and the heightened perception of property rights insecurity. Using estimates of wood production and legal logging, I also find that
the presence of transmigrants is correlated with illegal logging.
Keywords: Deforestation, Migration, Social Cohesion, Property Rights
JEL Classification: Q23, O15, J15
∗
Email: [email protected]
1
1
Introduction
The Indonesian Transmigration Program is one of the world’s largest governmentsponsored voluntary migration programs. Before its decline in 1998, it succeeded
in migrating over 4 million individuals from the fertile but densely populated inner
islands of Indonesia to the sparsely populated but heavily forested outer islands of the
country.1 The program was started in 1905, by the Dutch colonial government, halted
during the Japanese occupation, and revived during the presidencies of Sukarno and
Suharto.2 At its height, the program migrated over 2 million individuals within 4
years, and its source of funding extended to international organizations such as the
World Bank and the Asian Development Bank. It was also during this time that
the program became controversial, in part due to its effect on deforestation (World
Bank 1990; Food and Agriculture Organization of the United Nations 1990; Dick
1991; Dauvergne 1993).
The most cited work examining the effects of transmigration on deforestation is
an article by Dick (1991). He conjectures that the effect of transmigration on deforestation must have been 0.02 - 0.03 km2 of forested area per transmigrant family:
0.02 km2 as settlement for the family (this was the amount of land the government
gave to each family) and an assumed 0.01 km2 per family for infrastructure support.
To the best of my knowledge, there has been no attempt to empirically estimate
1
The inner islands are composed of the islands of Java, Bali and Madura while the outer islands
are composed of the islands of Sumatra, Kalimantan, Sulawesi, Maluku and West Papua. The goals
of the program were two-fold: (1) to decrease population pressure in the inner islands and (2) to
increase the country’s agricultural productivity.
2
Sukarno was president of Indonesia from 1945-1967. Suharto was president of Indonesia from
1967-1998.
2
whether transmigration actually caused widespread deforestation in Indonesia. This
paper examines both the short- and long-run effects of transmigration on deforestation using archival data, Landsat and MODIS satellite images, and a program
evaluation approach. I show a robust correlation between transmigration and deforestation both in the short- and long-run. The main mechanisms responsible for the
increased deforestation appear to be the disruption of local social cohesion and the
heightened perception of property rights insecurity. Using estimates of wood production and legal logging, I also find that the presence of transmigrants is correlated
with illegal logging.
The rest of the paper is structured as follows. Section 2 gives a brief background
on Indonesia and its Transmigration Program. The data is presented in Section 3
and the empirical approach and results in Section 4. I conclude in Section 5.
2
Background
The Transmigration Program reached its zenith during Suharto’s presidency, and
although the program continues to exist at present, the amount of money set aside
for the program and the number of individuals migrated has significantly declined.
Nevertheless, the link with deforestation remains a topic of ongoing debate. The
impact of the program is important because not only does Indonesia have one of the
world’s largest forest cover areas, most of which are found in the outer islands, it
also has one of the world’s highest deforestation rates (Margono et al. 2014). In fact,
since the 1990s, over 280,000 km2 of forest has been lost (Mongabay 2007), resulting
3
in loss of livelihood, mass extinction of species, and steadily increasing greenhouse
gas (GHG) emissions.3
The effect of the program on deforestation continues to be downplayed. Many
claimed that deforestation during Suharto’s presidency was not caused by the program. Rather, deforestation was caused by locals and their traditional farming techniques. Whitten (1987), in particular, claims that not all lands cleared to make room
for transmigrant settlements were forest land; many were degraded land or conversion forests, and as such, would have been converted anyways even if transmigration
never happened. Still, he concedes, especially since “[transmigrant] settlers are no
lovers of forests...[and] are quite happy to see [the forests] felled” (Whitten 1987,
p. 243), that if transmigration led to deforestation, the effect would be very small,
possibly just around 1% of total forest cover loss.
There were also those who defended the locals (Cramb 1988; Repetto 1990; Angelsen 1995). Angelsen (1995), in particular, finds that the effect of local farming
on deforestation has been “put out of proportion” [p. 1713]. The World Bank
(1990) and Food and Agriculture Organization of the United Nations (1990) go a
step further and directly blame the Transmigration Program for deforestation. They
estimate the program to have caused 2,500 to 3,000 km2 of forest cover loss every
year. Assuming that approximately 100,000 families were transmigrated every year,
these estimates coincide with Dick (1991)’s estimate of a forest cover lost of 0.03 km2
for each settled transmigrant family.
3
Of Indonesia’s total emissions of 3,014 metric tons of carbon dioxide equivalent (MTCO2e), 85%
is due to forest clearing and forest fires (United States Environmental Protection Agency 2008).
4
The Transmigration Program has been described as a bureaucratic mess, including by those in charge of the program (Cooney 2000). Not only was the program
characterized by conflicts between locals and transmigrants (Fearnside 1997; Adhikari
2003), it was extremely disorganized. Coordination between offices in the outer islands, who were examining land eligibility, and offices in the inner islands, who were
approving settlement locations, rarely occurred. Continuity was also a problem.
Between 1947 and 1966, for example, the ministry and department responsible for
transmigration changed ten times: from Manpower to Labor and Social Welfare to
Development and Youth to Home Affairs to Agrarian Affairs to Co-operatives, back
to Manpower and so forth.
There were several guidelines on how to pick transmigrants and their settlements. Many of these guidelines, however, were largely ignored and many settlement
inspections were done haphazardly. In fact, while one would expect transmigrant
settlements to be chosen based on land quality, given that one of the program’s goals
is to increase the country’s agricultural productivity, many chosen settlement lands
were not suited to the kind of wet rice agriculture that migrants from the inner islands are familiar with (Hardjono 1988). Tidal irrigation, as a method of bringing
fresh water to the rice fields using the tides, failed miserably. Crops died, and many
settlements could not survive without government support. When, after 3-5 years,
government support was withdrawn from these settlements, many agriculture plots
were abandoned and transmigrants took up other livelihoods.
The proposed and actual number of individuals transmigrated coincided with
highs and lows of economic and political events in Indonesia. Figure 1 shows a time-
5
line of economic and political events (bottom part of the timeline) vis-á-vis transmigration events (top part of the timeline). The height of the Transmigration Program
occurred during Suharto’s presidency, between late 1980s to early 1990s. During this
time, the goals and actual number of transmigrants migrated were at its highest (see
Table 1). Oil booms in 1973-1974 and 1979-1980 as well as external assistance from
the World Bank, the International Development Authority, UN specialized agencies
and bilateral donors increased the scale of transmigration. A decline in world oil
prices in 1987 and the 1997 Asian Financial Crisis did the opposite. In fact, when
Suharto resigned from presidency after the Asian Financial Crisis, the scale of the
Transmigration Program severely decreased.
Figure 1: Timeline of Indonesian Events
6
Table 1: Transmigration Figures, 1950-2000*
Program
Year
Goal
Actual
Pre-Repelita
1950-51 to 1969-70
Repelita I
1969-70 to 1974-75
Repelita II
1974-75 to 1978-79
Repelita III
1978-79 to 1983-84
Repelita IV
1984-85 to 1989-90
Repelita V
1989-90 to 1993-94
Repelita VI
1993-94 to 1999-00
38,141 families
250,000 families
1 million individuals
500,000 families
2 million individuals
750,000 families
3.75 million individuals
550,000 families
2.5 million individuals
600,000 families
-
100,000 families
36,483 families
182,414 individuals
118,000 families
544,688 individuals
535,000 families
2,469,560 individuals
230,000 families
1,061,680 individuals
n/a
n/a
≤300,000 families
≤1.5 million individuals
Notes: During Suharto’s presidency, transmigration was done in repelitas of Five-Year
Development Plans. Actual families and individuals moved were taken from Adhiati and
Bobsien (2001)
7
Suharto’s resignation not only virtually halted the Transmigration Program, it
also brought about a massive decentralization in Indonesia. Forest management, no
longer the direct responsibility of the national government, was now delegated to
districts. Indigenous people saw this as an opportunity to assert their adat 4 land
rights while miners and oil palm plantation businesses saw this as an opportunity to
cajole the bupati 5 to issue permits to destroy forests and convert them into mines or
plantations. These issues of forest property rights have significant and direct impacts
on forest conservation (Bohn and Deacon 2000; Sunderlin et al. 2014)
Present deforestation in the country has been attributed to decentralization (Burgess
et al. 2012), ethnic fragmentation (Alesina et al. 1999) leading to less social cohesion
(Ostrom et al. 1999; Katz 2000) and less local authority (Persha et al. 2011; Luttrell
et al. 2014), forest fires (Frankenberg et al. 2005), oil palm plantations (Koh and
Wilcove 2008), and mining activities (McMahon et al. 2000). Outside Indonesia,
deforestation has been shown to be highly correlated with migration (Browder 1995;
Naylor et al. 2002), population growth (Bilsborrow and Ogendo 1992), the presence
of roads and land quality (Pfaff 1999), and economic development and inequality
(Koop and Tole 2001). I use these to control for determinants of deforestation other
than transmigration.
4
Adat are customary or indigenous laws.
A bupati is the head of a kabupaten. A kabupaten is a regency, a second-level administrative
subdivision in Indonesia.
5
8
3
Data
To test whether transmigration led to deforestation in the outer islands, I use several
measures of deforestation and transmigration. Since disagreements on the determinants of deforestation in Indonesia stem mainly from the lack of appropriate and
primary data on the rate of forest cover change (Sunderlin and Resosudarmo 1996;
Romijn et al. 2013), I use four measures of deforestation, of which two come from the
interpretation of satellite images. I also use four different measures of transmigration:
two on a family level and two on an individual level.
The dataset on deforestation comes from Landsat satellite images, MODIS satellite images, and wood production volume and price, while the dataset on transmigrant families and individuals comes the Indonesian Ministry of Manpower and
Transmigration and collated from the Indonesian Statistical Yearbooks and Pocketbooks. To control for other determinants of deforestation in a cross-section setting,
I include measures of standing forest, district splits, roads, oil palm plantations,
mining, poverty, income, gender, soil quality and tidal irrigation.
Using either Landsat or MODIS satellite images has its own advantages and
disadvantages. Landsat goes as far back as 1972, although images before 2000 in
the U.S. Geological Survey is sparse. Missing images, hence, missing districts and
years, make it hard to obtain a good measure of deforestation before the 21st century. Moreover, although Landsat images have finer resolutions than MODIS images6
many Landsat images are laden with clouds and cloud shadows, making it hard to
6
A pixel is equivalent to 30-60 square meters in Landsat while a pixel is equivalent to 250 square
meters in MODIS.
9
distinguish these from deforestation. MODIS satellite images, on the other hand,
provide cloudless images but is only available starting 2000. This paper uses both
datasets. The Landsat dataset provides a measure of deforestation in the short-run
while the MODIS dataset provides a measure of deforestation in the long-run. Table
2 shows the summary statistics for all four measures of deforestation, and a more
detailed description for each measure is discussed below.
Table 2: Deforestation Summary Statistics
Variable
Category
Obs.
Landsat (km )
Province
31
MODIS (m2 )
Conversion
Conservation
Production
Protection
Others
Wood Volume (m3 )
Wood Price (U S$)
District
District
District
District
District
District
Province
Province
9352
1184
1520
2096
2152
2400
239
239
2
Years
Mean
1994, 1997, 10874.36
and 1998
2001-2008
33719.2
2001-2008
44107.9
2001-2008 11541.61
2001-2008
67615.7
2001-2008 7667.867
2001-2008 36396.35
1994-2007
800976
1994-2007
649.56
Std. Dev.
Min.
Max.
11709.61
0
39045.36
131402.3
123167.4
64278.08
214001.8
30780.58
119909.8
1106679
527.19
0
0
0
0
0
0
6571
0
3691000
1355500
1471000
3691000
1000500
2363500
5606388
123452
Notes: The MODIS dataset has deforestation data for 311 districts from 2001 to 2008. There are
9352 observations in MODIS in total because deforestation in the dataset has also been divided
into forest zones. The sum of district deforestation in all forest zones (conversion, conservation,
production, protection, and others) equal total district deforestation. Wood volume and price in
the original dataset has 3082 observations, with type of wood per province per year as a unit of
observation. For this paper’s purposes, the sum of wood volume and price for all types of wood per
province per year is used. Wood prices have been converted in 2000 USD.
1. Landsat Satellite Images: Interpreted dataset from Landsat satellite images
comes from the Indonesian Ministry of Forestry’s “Forest Statistics: Division
of Forest Inventory and Land Use” (Statistik Kehutanan: Bidang Inventarisasi
Dan Tata Guna Hutan). Data is available for the years 1986, 1994, 1997 and
1998, and contains the area that did not have cloud cover and hence, was
10
successfully interpreted. This area is divided into either forested area or nonforested area for each province, with maximum deforestation occurring at South
Sumatra in 1986.
2. MODIS Satellite Images: Another measure of deforestation is the number of forested pixels lost using MODIS satellite images. Deforestation is in
terms of forested pixels lost per Indonesian district from 2001 to 2008. This
dataset presents deforestation numbers for 311 districts7 . These 311 districts
are divided into five categories of forest zones: (1) conversion forests, (2) conservation forests, (3) production forests, (4) protection forests, and (5) forests
for other uses.8 Dropping the “forest for other uses” category, 300 districts
remain, with 148 districts under conversion forests, 190 districts under conservation forests, 262 districts under production forests, and 269 districts under
protection forests. Appendix A provides a summary of the number of districts for each forest zone in each province. On average, Indonesia has lost
33,720 square meters of forest from 2001 to 2008, with a standard deviation of
7
There are no deforestation numbers for all districts in the inner islands and some transmigration
villages were not matched to deforestation districts in Riau Island and the Moluccas. Moreover,
although Indonesia differentiates between cities and districts (both are considered second-level administrative subdivisions), in this paper, cities are counted as a separate district and I do not
differentiate between a city and a district.
8
Forest zones are areas designated by the Ministry of Forestry in accordance with The Forestry
Law (No. 14/1999) and the Basic Forestry Law (No. 5/1967). Production and conversion zones
are those in which legal logging is allowed and negotiations take place between logging companies
and community representatives. While production zones are devoted to the extraction of timber
subjected to the granting of a logging permit, in conversion zones authorized companies can clearcut the forest to set up plantations for industrial timber, oil palm and other estate crops (Barr et al.
2006; Alesina et al. 2014).
11
131,402 square meters. Deforestation at 3,691,000 square meters is highest at
East Kotawaringin, Central Kalimantan in 2006.
3. Wood Production Volume and Price: Wood production volume and price
from 1994-2007 is also used as a measure of deforestation. These statistics are
collated from the annual “Statistics of Forest and Concession Estate” (Statistik
Perusahaan Hak Pengusahaan Hutan) published by the Indonesian Central
Bureau of Statistics. It contains the quantity and value of logs cut for 19
provinces and the associated provincial price by type of wood for 117 different
types of wood. Because the dataset is derived from production, it includes both
clear felling as well as selective logging; on the other hand, it captures only
logging that was officially reported by the forest concessions, and so likely does
not include most illegal logging. This dataset is mainly used as a robustness
check, especially since misclassification of pixels is possible when analyzing
satellite images9 .
Like deforestation, several measures are also used for transmigration depending
on the unit measure as well as on how big the administrative divisions are. Table 3
shows the summary statistics for the transmigration measures and below is a more
in-depth discussion of these four measures.
1. Village Transmigrant Families and Individuals: The Indonesian Ministry
of Manpower and Transmigration (MOTM) provides a roster of ex-transmigration
9
Misclassification is especially possible when using Landsat Satellite images. Images that contain
clouds make it hard to analyze whether deforestation or reforestation has happened between years.
12
Table 3: Transmigration Summary Statistics
Mean
Village T.Families
2,543.00
Village T.Individuals
10,955.76
Provincial T.Families
42,345.14
Provincial T.Individuals 56,043.24
Std. Dev.
Min.
Max.
5,826.75
25,212.50
41,111.85
86,531.35
0
0
0
0
58,145.00
255,173.00
162,169.00
407,422.00
locations that have become villages or district capitals (Daftar Nama-Nama Eks
Lokasi Transmigrasi Yang Telah Menjadi Desa/Ibu Kota Kecamatan). This
dataset contains a list of transmigration villages that were successfully transferred to the transmigrants and have since then been able to survive without
government support. It also contains the district these villages were in, the
placement and transfer dates10 , the number of transmigrant families and individuals placed in a village and the number of transmigrant families and individuals during the village transfer. The earliest placement date is 1952 and the
latest is 2006. Fifteen out of the 33 provinces in Indonesia have transmigrant
settlements that became villages or district capitals, with West Sumatra having the lowest number of these villages and South Sumatra having the most.
Appendix B provides a summary of the number of villages in each province
that were successfully transfered from the government to the transmigrants.
2. Provincial Transmigrant Families and Individuals: The Statistical Yearbooks and Pocketbooks of Indonesia also provide transmigration data at a
provincial level from 1856-1994. Appendix D summarizes the years for which
10
The regression analysis uses the number of families “transferred” rather than “placed” since
the analysis is interested in the transmigrants who settled in the outer islands.
13
transmigration data has been made available in these yearbooks and pocketbooks and in which yearbooks and pocketbooks these data can be found
in. Since transmigration declined after Suharto resigned from presidency, the
missing years after 1994 should not greatly affect the analysis. Unfortunately,
because of the missing years between 1956 to 1994, the total number of transmigrant families and individuals per repelita (see Appendix C) do not match
the numbers in Table 1. Furthermore, the number of transmigrant individuals
was not made available in the yearbooks and pocketbooks for the years 1988
to 1994.
Figures 2a and 3a plot district and provincial deforestation using MODIS from
2001-2008 on a map of Indonesia. Below these deforestation figures are similar plots
for the number of transmigrant families and individuals. The district level heat
maps are constructed by summing the number of village transmigrant families and
individuals (respectively) for all available years while the province level heat maps
are constructed by summing the number of provincial transmigrant families and
individuals (respectively) for all available years. Since MODIS is on a district level,
I use the provincial sum of district deforestation in plotting provincial deforestation.
For both heat maps, I find that deforestation is highly correlated with both the
number of transmigrant families and individuals. In many districts where the heat
map shows high levels of deforestation, there are similarly high levels of transmigrant
families and individuals.
As for the control variables, an extensive review of related literature shows that
the following should be controlled for: standing forest, decentralization, roads, min14
(a) District Deforestation
(b) Number of Families Transmigrated
(c) Number of Individuals Transmigrated
Figure 2: MODIS Deforestation and Village T.Families and T.Individuals
15
(a) Provincial Deforestation
(b) Number of Families Transmigrated
(c) Number of Individuals Transmigrated
Figure 3: MODIS Deforestation and Provincial T.Families and T.Individuals
16
ing and oil palm plantations, economic development and inequality, gender, soil quality and tidal irrigation. Table 4 lists these control variables in alphabetical order,
their descriptions, the year used, and where they are from.
Table 4: Control Variables
Code
Variable Name
Area
District Land Area
Provincial Land Area
DSplits
District Splits
Forest
Forest Area
Gender
Gender
Income
Income
Mining
Mining
Plantations Oil Palm Plantations
Pop
Rural Population
Provincial Population
Poverty
Poverty
Roads
Roads
Soil
Soil quality
Tidal
Tidal Irrigation
Variable Description
Level
Year
Source
Total area in square meters
Total area in square meters
Average district splits
Average standing forest
Percentage of males
Regional GDP
Individuals in the mining sector
Total area of oil palm plantation
Population in the rural areas
Population in the province
Individuals below the poverty line
Length of roads: asphalt, dirt, gravel
Soil quality: low, medium, high
1 if inland and 0 otherwise
District
Province
District
District
Province
Province
District
District
District
Province
District
District
District
District
2000
2000
2001-2008
2000
2009
2004
2007
2000
2000
2000
1999
2001
1993
N/A
WB
BPS
Burgess
MODIS
BPS
BPS
WB
WB
WB
BPS
WB
WB
USDA
N/A
Notes: WB stands for the World Bank, BPS stands for the Indonesian Bureau of Statistic (Badan Busat
Statistik ), Burgess stands for Burgess et al. (2012), MOTM stands for the Indonesian Ministry of Manpower
and Transmigration, and USDA stands for United States Department of Agriculture.
4
4.1
Results
Short Run Effects
To examine the effect of transmigration in the short run, I begin by estimating the
following equation:
DEFi,t = α + βT RAN Si,t−1 + F Ej + i,t
17
(1)
where DEFi,t is deforestation in square kilometers in province i in island j at time
t, T RAN Si,t−1 is the number of transmigrants moved to province i at time t − 1,
F Ej are island fixed effects, and is the error term. Since I have forest covers for
1986, 1994, 1997 and 1998 from Landsat satellite images, I am able to compute
deforestation for 1994, 1997 and 1998.
Table 5 reports the results for provinces in the outer islands that underwent deforestation. Linear regressions using the data on village transmigration show that an
additional transmigrant individual leads to 0.7 km2 of forest cover loss in a year while
an additional transmigrant family leads to 3.42 km2 of forest cover loss. Although
still statistically significant, these effects decrease with island fixed effects. Linear regressions using data on provincial transmigrant families report a smaller magnitude
that becomes smaller with island fixed effects: an additional transmigrant family
leads to 0.88 km2 of forest cover loss without fixed effects and 0.70 km2 of forest
cover loss with island fixed effects. Unfortunately, the data on provincial transmigrant individuals do not have sufficient observations to do a similar regression.
As for the log specification, results are significant and positive. But instead of
having a decresing magnitude when fixed effects are included, magnitudes increase
for village transmigrant family, village transmigrant individual, and provincial transmigrant family.
18
Table 5: Short Run Panel Regression Results
Dependent Variable: Deforestation in 1994, 1997 and 1998 (km2 )
(1)
T.Fam
Constant
R-squared
N
Province FE
Island FE
3.4248**
(0.6293)
9231.0082
(3949.4688
0.10
31
no
no
(7)
T.Ind
Constant
R-squared
N
Province FE
Island FE
0.7028**
(0.1410)
9,379.1949*
(3937.7596)
0.09
31
no
no
(13)
T.Fam
Constant
R-squared
N
Province FE
Island FE
0.8795***
(0.1082)
7372.2831
(3307.3544)
0.25
31
no
no
Village Transmigrant Families
Linear Regression
Log Regression
(2)
(3)
(4)
(5)
2.0455
(3.2836)
9,892.8623***
(1575.6146)
0.60
31
yes
no
2.9005***
(0.2945)
9,482.5975***
(141.3223)
0.32
31
no
yes
0.2989*
(0.1237)
19.2037***
(1.0033)
0.02
31
no
no
0.7545**
(0.1403)
17.9732***
(0.3790)
0.37
31
yes
no
Village Transmigrant Individuals
Linear Regression
Log Regression
(8)
(9)
(10)
(11)
0.3398
(0.6931)
10,151.4880***
(1474.4090)
0.59
31
yes
no
0.5506***
(0.0840)
9,702.9392***
(177.1152)
0.3
31
no
yes
0.2057
(0.1563)
19.3757***
(0.7848)
0.02
31
no
no
0.6666***
(0.0572)
17.9521***
(0.1767)
0.37
31
yes
no
Provincial Transmigrant Families
Linear Regression
Log Regression
(14)
(15)
(16)
(17)
0.5689
(0.5154)
8,609.1434**
(2052.1196)
0.64
31
yes
no
0.7034***
(0.0603)
8,073.6184***
(240.1868)
0.4
31
no
yes
0.6710**
(0.176)
17.5313***
(1.6029)
0.2
31
no
no
0.7781*
(0.2469)
17.1354***
(0.9125)
0.49
31
yes
no
(6)
0.3928**
(0.0814)
18.9502***
(0.2198)
0.11
31
no
yes
(12)
0.2457
(0.1349)
19.2522***
(0.4167)
0.09
31
no
yes
(18)
0.7192**
(0.1744)
17.3530***
(0.6445)
0.27
31
no
yes
*** p < 0.01, ** p < 0.05, * p < 0.10
Notes: Robust standard errors, clustered at the island level in parentheses. “T.Fam” stands for
transmigrant family. “T.Ind” stands for transmigrant individual. Since deforestation is at the province
level, the number of village transmigrants per year within a province were summed up.
19
Since height of transmigration happened before the 1990s, I also estimate the
short run effect of transmigration at its height by estimating the following equation
for deforestation between 1986 and 1994:
DEFi,1994 = α + βT RAN Si,1986 + i,1994
(2)
Using the data on village transmigrant families and individuals, results show that
an additional transmigrant individual leads to 0.32 km2 of forest cover loss while
an additional transmigrant family leads to 1.37 km2 of forest cover loss (see Table
6). However, although still positive, coefficients in the log specification are no longer
statistically significant. Positive results from the linear model could possibly be due
to outliers. Hence, I ran robust linear regressions to check if this is the case. With
robust regressions, coefficients continue to be statistically significant, with slightly
higher magnitudes compared to the coefficients in the standard linear regressions.
Interestingly, doing the same kind of analysis but running it using data on provincial
transmigrant families, I find positive and statistically significant effects of an additional transmigrant family on deforestation both in the robust linear and robust log
specifications.
20
Table 6: 1994 Cross Section Regression Results
Dependent Variable: Deforestation (Def1994 , km2 )
Standard Regressions
Linear Regression
Log Regression
Village Transmigrants
Provincial Transmigrants Village Transmigrants Provincial Transmigrants
(1)
(2)
(3)
(4)
(5)
(6)
T.Fam
1.3814*
(0.722)
0.3217*
(0.1572)
Constant 15,607.1227*** 15,544.8946***
(3803.1726)
(3690.6275)
R-squared
0.09
0.09
N
13
13
0.5338
(0.3551)
0.0436
(0.0368)
T.Ind
13,280.3454**
(5207.3409)
0.18
13
23.2312***
(0.2346)
0.05
13
0.1772
(0.1465)
3.510
(0.031)
23.2407***
(0.2305)
0.05
13
21.9492***
(1.3511)
0.12
13
Robust Regressions
Linear Regression
Log Regression
Village Transmigrants
Provincial Transmigrants Village Transmigrants Provincial Transmigrants
(7)
(8)
(9)
(10)
(11)
(12)
T.Fam
1.7845**
(0.716)
0.4026**
(0.1656)
Constant 10,943.6356*** 11,005.3405***
(2203.6253)
(2238.0752)
R-squared
0.36
0.35
N
13
13
0.7154***
(0.2285)
0.0438
(0.0604)
T.Ind
8,705.4343**
(2961.4854)
0.47
13
23.2268***
(0.4163)
0.05
13
0.2684*
(0.1486)
0.0350
(0.052)
23.2381***
(0.428)
0.04
13
21.0811***
(1.3131)
0.23
13
*** p < 0.01, ** p < 0.05, * p < 0.10
Notes: Robust standard errors in parentheses. Deforestation is computed as the difference in forest cover between 1985 and 1994.
“T.Fam” stands for provincial transmigrant family in 1985.
21
4.2
Long Run Effects
To analyze long-run effects of transmigration on deforestation, I estimate the following equation:
DEFi,j,t = α + β
t
X
T RAN Si,s + P ROVj + Y EARt + i,j,t
(3)
s=1952
where i is the district, j is the district, the province or the island, and t is the year.
DEFi,j,t is deforestation in district i in province or island j at year t; T RAN Si,s is the
number of transmigrants moved to district i; P ROVj are district-level, province-level
or island-level fixed effects; Y EARt are year fixed effects; and i,j,t is the error term.
The long-run analysis makes use of the deforestation numbers from MODIS satellite
images. Hence, with MODIS, t goes from 2001 to 2008. The MODIS dataset also
allows for the estimation of the relationship between transmigration and deforestation
under different forest zones. As for transmigration, although there are four measures
of transmigration, the time dimension of the dataset only allows for the use of two
measures - the number of transmigrant families and individuals settled in a village.
22
Table 7: Long Run Regression Results
Dependent Variable: Deforestation from 2001 to 2008 (m2 )
T.Fam
Constant
R-squared
N*T
Year FE
District FE
Province FE
Island FE
Village Transmigant Families, 1952 - 2006
Linear Regression
Log Regression
(1)
(2)
(3)
(4)
(5)
(6)
(7)
16.8500**
16.1471**
11.1674*
15.3682**
0.5056*** 0.3243*** 0.3405***
(7.2008)
(7.5454)
(5.6361)
(6.9707)
(0.0646)
(0.0626)
(0.0491)
74,799.5101***
0
0
0
6.7235***
0
0
(21430.8515) (28266.5897) (28015.0495) (15238.3376) (0.4281)
(0.3002)
(0.2848)
0.08
0.54
0.27
0.16
0.22
0.84
0.64
2799
2799
2799
2799
2799
2799
2799
no
yes
yes
yes
no
yes
yes
no
yes
no
no
no
yes
no
no
no
yes
no
no
no
yes
no
no
no
yes
no
no
no
23
(9)
T.Ind
Constant
R-squared
N*T
Year FE
District FE
Province FE
Island FE
3.8695**
(1.6834)
75,181.5054***
(21692.0005)
0.08
2799
no
no
no
no
Village Transmigant Families, 1952 - 2006
Linear Regression
Log Regression
(10)
(11)
(12)
(13)
(14)
(15)
3.7071**
(1.7646)
0
(28221.37)
0.54
2799
yes
yes
no
no
2.5141*
(1.3047)
0
(28048.7519)
0.27
2799
yes
no
yes
no
3.5243**
(1.6287)
0
(15215.4262)
0.16
2799
yes
no
no
yes
0.2820***
(0.0506)
0
(0)
0.57
2799
no
no
no
no
0.2802***
(0.0533)
0
(0.3016)
0.84
2799
yes
yes
no
no
0.2915***
(0.0408)
0
(0.2860)
0.64
2799
yes
no
yes
no
(8)
0.3526***
(0.0511)
0
(0.2127)
0.6
2799
yes
no
no
yes
(16)
0.3004***
(0.0434)
0
(0.2117)
0.6
2799
yes
no
no
yes
*** p < 0.01, ** p < 0.05, * p < 0.10
Note: Robust standard individuals, clustered at the province level in parentheses. “T.Fam” stands for transmigrant family
and “T.Ind” stands for transmigrant individuals.
Table 7 reports the results from running equation 3 in linear and log specifications.
One observation represents a district and deforestation is measured in square meters
(m2 ). Columns (1), (5), (9), and (13) are results without any fixed effects. The rest
have year fixed effects and district, province or island fixed effects.
Deforestation is consistently higher in areas with more transmigrants. In the
linear regression, an additional transmigrant individual causes 2.51 m2 of forest cover
to disappear in a year at very least. Assuming that each transmigrant family is
composed of 3-4 members, this effect is less than the marginal forest cover loss due
to an additional transmigrant family. Column (3) shows that, at the very least, an
additional transmigrant family leads to 11.17 m2 of forest cover loss in a year.
Results for log regressions are also consistently positive. However, the coefficients
from the log regressions imply a bigger long-run effect of transmigration on deforestation. Given that the mean of deforestation in this specification is 112,662.40
m2 and the mean of transmigrant individuals is 9,686 individuals, 0.2802 in Column
(14), the smallest effect among the log regressions, implies that an additional transmigrant individual leads to 3.26 m2 for forest cover loss. Moreover, given that the
mean of transmigrant family is 2,257 families, 0.3243 in Column (6) implies that an
additional transmigrant family leads to 16.26 m2 of forest cover loss.
Using the same dataset, I am also able to estimate the long-run cumulative effect
of deforestation by running a cross-section regression of the sum of past transmigration on the sum of present deforestation. Hence, the following estimation model:
2008
X
t=2001
DEFi,j,t = α + β
2006
X
T RAN Si,t + γCON Tj + i,j
t=1952
24
(4)
where
P2008
t=2001
DEFi,j,t is the sum of deforestation from 2001 to 2008,
P2006
t=1952
T RAN Si,t
is the sum of transmigration from 1952 to 2006, CON Tj are district-level and
provincial-level controls mentioned in Table 4, is the error term, i is the district,
and j is either the district. The coefficient of interest is β, and it measures the effect
of an additional transmigrant moved between 1952 to 2006 on deforestation between
2001 and 2008.
Table 8 report the results from running equation 4. An observation represents
a district. For deforestation, it is one district from 2001 to 2008. For transmigration, it is one district from 1952 to 2006. Just like the previous long-run analysis,
deforestation is measured in m2 .
Results from the linear specification show that an additional transmigrant individual in the past leads to present forest cover loss of 29.66 m2 while an additional
transmigrant family in the past leads to present forest cover loss of 129.18 m2 . This
result, however, disappears when control variables are added. In the log specification,
the effect of an additional transmigrant individual drops from a coefficient of 0.2027
(equivalent to 18.86 m2 ) to 0.1746 (equivalent to 16.24 m2 ) while the effect of an additional transmigrant family drops: the coefficient decreases from 0.2369 (equivalent
to 95 m2 ) to 0.2048 (equivalent to 82.12 m2 ).11 The statistical significance of the
coefficients for transmigrant individuals and families drop when controls are added
but continue to be statistically significant.
11
In this specification, mean deforestation is 1,013,961 m2 , mean number of transmigrant individuals is 10,897.56 individuals in this specification, and mean number of transmigrant families in
this specification is 2,528.55 families.
25
Table 8: Cumulative Cross Section Regression Results
P
2
Dependent Variable: Deforestation ( 2008
t=2001 DEFt , m )
(1)
T.Fam
Village Transmigrant Families 1952 - 1979, 1980 - 2006
Linear Regression
Log Regression
(2)
(3)
(4)
(5)
129.1786***
(47.7025)
T.Fam 1
T.Fam 2
NTFam
Constant
687,326.8204***
(120934.1702)
R-squared
0.14
N
311
Controls
no
34.5202
(38.6108)
0.2369***
(0.0244)
-103.8669
(73.8917)
103.2229**
(51.2593)
40.4475***
(13.6161)
-26,991,422.1385**
8770722.9596
11.4816***
(13063568.5114)
(20223006.7133)
(0.1431)
0.39
0.48
0.22
67
67
308
yes
yes
no
0.2048**
(0.0847)
-0.0289
(0.0502)
0.1490*
(0.0815)
0.1122
(0.0842)
-27.0005* -25.2191
(15.4333) (15.5565)
0.59
0.63
67
67
yes
yes
Village Transmigrant Individuals 1952 - 1979, 1980 - 2006
Linear Regression
Log Regression
(7)
(8)
(9)
(10)
(11)
T.Ind
29.6581***
(11.1668)
7.9138
(9.0708)
T.Ind 1
T.Ind 2
NTInd
Constant
R-squared
N
Controls
690760.1507***
(122346.1306)
0.1400
311
no
-26856917.9599
(20656904.6773)
0.39
67
yes
0.2027***
(0.0208)
-23.9726
(16.9966)
25.0529**
(12.214)
9.3740***
(3.1078)
10520314.068
11.4786***
(20765541.1957)
(0.1425)
0.49
0.22
67
308
yes
no
(6)
(12)
0.1746**
(0.0729)
-0.0210
(0.0424)
0.1315*
(0.0708)
0.0911
(0.0751)
-27.2834* -25.6750
(15.4922) (15.3744)
0.59
0.63
67
67
yes
yes
*** p < 0.01, ** p < 0.05, * p < 0.10
Note: Robust standard errors in parentheses. The signs of control variables that are statistically significant
are as expected except for roads and decentralization. Roads showed a negative coefficient which was
statistically significant for regressions in columns (2), (4), (8), and (10). Decentralization showed negative
and statistically significant coefficients for regressions in columns (5), (6), (11) and (12). The magnitude
of the coefficient for decentralization, however, is very small, and is positive when it is not statistically
significant.
26
I further analyze the effect of transmigration by dividing transmigrants into newer
and older migrant groups and by computing the total number of transmigrants moved
to neighboring districts. By dividing transmigrants into older and newer migrant
groups, I will be able to examine whether older migrants still cause deforestation.
Since they have been in the outer islands longer, older transmigrants (as opposed to
newer, not younger) might have integrated with the local society more than the newer
transmigrants. Hence, this division of transmigration can possibly proxy for social
cohesion. T.Ind 1 and T.Fam 1 are the number of transmigrant individuals and
families migrated between 1952 and 1979 (respectively) while T.Ind 2 and T.Fam 2
are the number of transmigrant individuals and families migrated between 1980 and
2006 (respectively).
On top of dividing migrants into old and new, I also use the number of neighboring transmigrants to proxy for perceived property rights insecurity. The idea is
to compare districts that are absolutely identical in every dimension except that one
district have transmigrants migrating into neighboring districts while the other does
not. Individuals who witness migrants moving in other neighboring districts should
perceive their property rights to be insecure. NTInd and NTFam are the number
transmigrant individuals and families moved in districts that share a border with
district i for all i’s.
Results for this further analysis are shown in columns (3), (6), (9) and (12)
in Table 8. Notice that older transmigrants do not statistically significantly affect
deforestation but newer transmigrants do. There is also a property rights effect. An
additional neighboring transmigrant individuals leads to an increase in deforestation
27
by 9.37 m2 while an additional neighboring transmigrant family leads to an increase
in deforestation by 40.45 m2 . To ensure the consistency of my results, I also run
regressions with transmigrants divided into 5-year and 10-year groups. Results from
these regressions are generally consistent with results from dividing transmigrants
into 2 groups.
4.3
Illegal Logging
I also estimate equation 3 using Wood Production and Price as a proxies for deforestation. Using this wood dataset, t goes from 1994 to 2007. Moreoever, since it is on
a provincial level, I sum up all village transmigrants belonging to the same province
and use that as a measure of provincial transmigration. Wood volume is measured
in cubic meter m3 and wood price is measured as provincial price of a particular type
of wood converted to 2000 USD.
Results show that in general an increase in transmigrant families and individuals
decreases wood production volume and price. This effect on wood production price
is expected (See Table 9). If transmigration causes deforestation, an increase in the
number of transmigrants should shift the supply of wood to the right and decrease
price. However, a similar decrease in wood price is initially puzzling since one would
expect an increase in wood production volume to increase prices. The key point in
understanding this effect is the fact that the wood production variable only measures
legal wood production. It can be inferred that since wood price decreased, there
should be an increase in the general supply of wood. If legal wood supply decreased
28
Table 9: Wood Production Regression Results
Dependent Variable: Wood Production Volume or Provincial Price (in USD)
T.Fam
Constant
R-squared
N*T
Province FE
T.Ind
Constant
R-squared
N*T
Province FE
Linear Regression
Wood Volume Wood Price
(1)
(2)
-127.4334***
-0.0741***
(42.2375)
(0.0138)
5519353***
3385.3430***
0.8233
0.656
239
239
yes
yes
Linear Regression
Wood Volume Wood Price
(1)
(2)
-31.1419**
-0.0175***
(11.2341)
(0.0030)
5773172***
3437.8790***
0.8232
0.6528
239
239
yes
yes
Log Regression
Wood Volume Wood Price
(3)
(4)
-5.9413**
-2.987***
(2.1148)
(1.1889)
60.8100***
30.400***
0.8271
0.7641
239
239
yes
yes
Log Regression
Wood Volume Wood Price
(3)
(4)
-5.9381**
3.5144**
(2.4908)
(1.372494)
67.5132***
36.6486***
0.8208
0.7645
239
239
yes
yes
*** p < 0.01, ** p < 0.05, * p < 0.10
Note: Robust standard errors, clustered at the province level in parentheses.
“T.Fam” stands for transmigrant family. “T.Ind” stands for transmigrant individual. Wood volume is measured in m3 and wood price is in 2000 USD.
but the general wood supply increased, it could only mean that the supply of illegal
wood increased.
Estimating equation 3 using the MODIS dataset divided into forest zones, I further find support that not only does transmigration lead to deforestation, it also increases illegal logging. Table 10 shows that even conservation and protection forests,
forest areas that are not supposed to be logged, are negatively affected by increase
transmigrants.
29
Table 10: Regression Results By Forest Zone
Conversion
Conservation
Production
Protection
Others
(+)
(-)
(+)
(+)
(-)
(+)
(-)
(+)*
(+)
(+)
(+)
(+)
(-)
(+)
(-)
(+)
(+)
(+)*
(+)
(+)*
(+)
(+)
(-)
(+)
(+)
(+)
(+)
(+)*
(+)**
(+)*
(+)*
(+)
(-)
(+)
(+)
(-)
(+)*
(+)**
(+)**
(+)
(+)***
(+)
(+)**
(+)
(+)
(+)***
(+)
(+)***
(+)*
(+)***
(+)*
(+)***
(+)
(+)
(+)
(+)*
(+)
(+)***
(+)
(+)***
Long-run Cross Section with Controls
(1) T.Fam, Linear
(2) T.Fam, Log
(3) T.Ind, Linear
(4) T.Ind, Log
Long-run Cross Section Channels with Controls
(1) T.Fam, Linear (T.Fam 2)
(2) T.Fam, Linear (NTFam)
(3) T.Fam, Log (T.Fam 2)
(4) T.Fam, Log (NTFam)
Long-run Panel, Province and Time FE
(1) T.Fam, Linear
(2) T.Fam, Log
(3) T.Ind, Linear
(4) T.Ind, Log
*** p < 0.01, ** p < 0.05, * p < 0.10
Note: Robust standard errors in parentheses. Standard
errors for the panel regressions are clustered at the province level.
P
Dependent variable for the cross section regression is 2008
t=2001 DEFt while the dependent variable for the panel regression is DEFt
where t = 2001 to 2008. “(+)” signifies a positive coefficient. “(-)” signifies a negative coefficient. “T.Fam” stands for transmigrant
families. “T.Ind” stands for transmigrant individuals.
4.4
Selection Bias Problem
There is a concern that certain districts might have been more likely to receive transmigrants than other districts. The same districts might also be more likely to be
deforested. To address this concern, I calculate each district’s propensity to receive
transmigrants using metrics that government bureaucrats used to make decisions.
These metrics include soil quality, the existence of a shore for tidal irrigation, population, poverty and length of roads. Since the height of transmigration happened
in the late 1980s, early 1990s, I use variables that measure these metrics around this
time period. Looking at districts with similar propensities of getting transmigrants,
I regress deforestation on whether or not a district was used as a transmigration settlement or not. Columns (1) and (2) of Table 11 shows the linear and log regression
results using propensity score matching.
30
Table 11: PSM and Heckman Correction Results
P
2
Dependent Variable: Deforestation ( 2008
t=2001 Deft , m )
Transmigration
Constant
R-squared
N
Propensity Score Matching Results
Linear Regression Log Regression
(1)
(2)
Heckman Correction Results
Linear Regression Log Regression
(3)
(4)
1,844,254.4298***
(372399.456)
186,548.3871***
0.14
133
1,812,683.9819***
(392404.4435)
327377.6
2.3837***
(0.3583)
11.16916
265
265
2.6329***
(0.3355)
10.7360***
0.33
131
*** p < 0.01, ** p < 0.05, * p < 0.10
Note: Robust standard errors in parentheses. The variable “Transmigration” is a dummy: 1
if a district ever had transmigrants and 0 otherwise.
Table 11 also shows results from running deforestation on a transmigrantion
dummy using Heckman correction. Just like the propensity score matching approach,
the selection criteria used to run Heckman correction includes soil quality, the existence of a shore for tidal irrigation, population, poverty and length of roads. Both
results from the propensity score matching and Heckman correction show statistically significant coefficients for transmigration. This means that looking at fairly
similar districts, districts with transmigrants led to higher deforestation.
5
Conclusion
Although there are individuals who have written to the contrary, the general belief
has always been that the Indonesian Transmigration Program, a massive governmentsponsored and -incentivized migration program, has led to deforestation in the outer
islands of Sumatra, Kalimantan, Sulawesi and West Papua. This paper, to my knowl-
31
edge, is the first to properly empirically investigate whether this is the case. Using
Landsat satellite images in the 1990s and provincial and district level transmigration
data, I examine the relationship between deforestation and transmigration. Results
show that an additional transmigrant family causes 3.41 km2 (equivalent to 716
acres) of forest loss a year. Although this result is robust to changes in specification
and the inclusion of district or province fixed effects, the coarseness of the deforestation data makes it difficult to further examine the relationship between deforestation
and transmigration. Perhaps a finer deforestation dataset around this time period
and a more sophisticated way of analyzing forest cover given the quality of Landsat
satellite images could be a worthy future endeavor.
This paper also examines the relationship of deforestation and transmigration in
the long run. Panel regressions show that an additional transmigrant family causes
11.16 m2 of forest loss a year. This number is significantly and understandably
less than the short run effect. Further analysis using cross section regressions show
that this persistent long run effect of transmigration on deforestation is due to the
disruption of social cohesion and the perception of more insecure property rights. In
particular, more socially incohesive districts were more likely to cause deforestation.
Individuals who witnessed more transmigrants moved in districts neighboring theirs
were also more likely to deforest more.
Using wood production volume and provincial price as proxies for deforestation,
I find that not only does transmigration lead to deforestation, it also increases illegal logging. Panel regressions with provincial fixed effects show that an additional
transmigrant family leads to a decrease in both the volume of legal wood production
32
and the provincial price of wood. This implies that decrease in the provincial price of
wood can only because the total wood production, legal and illegal, have increased.
Understandably, transmigration is a country-specific event, that even in this specific country, is slowly being faced out. Results from this paper however shed light
on the effects of migration on deforestation and on resource use in general. Migration that disrupts social cohesion and increases the perceived insecurity of property
rights, can lead to increases in resource extraction, both through legal and illegal
channels.
33
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Appendices
A
MODIS Deforestation Per Forest Zone
Province Province
ID
Name
11
12
13
14
15
16
17
18
19
21
31
32
33
34
35
36
51
52
53
61
62
63
64
71
72
73
74
75
76
81
82
91
92
Aceh
Sumatera Utara
Sumatera Barat
Riau
Jambi
Sumatera Selatan
Bengkulu
Lampung
K. Bangka Belitung
K. Riau
DKI Jakarta
Jawa Barat
Jawa Tengah
Yogyakarta
Jawa Timur
Banten
Bali
NT Barat
NT Timur
Kalimantan Barat
Kalimantan Tengah
Kalimantan Selatan
Kalimantan Timur
Sulawesi Utara
Sulawesi Tengah
Sulawesi Selatan
Sulawesi Tenggara
Gorontalo
Sulawesi Barat
Maluku
Maluku Utara
Papua
Papua Barat
TOTAL
Districts in Burgess et al. (2012)
(1) (2) (3) (4) (5) ALL
1
12
19
22
23
18
8
26
27
33
11
14
14
17
19
12
9
12
6
11
3
10
10
7
11
7
9
12
11
15
0
10
7
8
10
0
9
10
11
14
0
0
6
7
7
10
11
13
13
14
14
5
14
9
9
9
0
9
11
13
0
8
13
11
13
1
12
11
14
15
10
10
11
11
11
2
11
21
22
24
8
9
11
12
12
3
6
6
6
6
2
1
5
5
5
26
25
21
28
24
11
11
11
11
11
148 190 262 269 300
23
33
19
12
11
15
10
14
7
14
14
13
13
15
11
24
12
6
5
29
11
311
Notes: (1) represents Conversion Forests, (2) represent Conservation
Forests, (3) represents Production Forests, (4) represents Protection Forests
and (5) represents Other Forests.
41
B
Successful Transmigrant Villages Per Province
Province
Number of Villages
Bengkulu
Gorontalo
Jambi
Kalimantan Barat
Kalimantan Selatan
Kalimantan Tengah
Kalimantan Timur
Lampung
Riau
Sulawesi Bara
Sulawesi Selatan
Sulawesi Tengah
Sulawesi Tenggara
Sulawesi Utara
Sumatera Barat
Sumatera Selatan
Sumatera Utara
98
48
151
130
107
200
134
121
265
79
59
161
204
26
13
284
46
42
C
Number of Transmigrant Families and Individuals from Statistical Yearbooks and Pocketbooks
Year
Number of Families
Number of Persons
1956
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1972
1973
1974
1978
1979
1980
1981
1988
1989
1990
1991
1992
1993
1994
TOTAL
5,765
5,158
6,255
11,439
5,702
5,064
7,692
3,425
13,049
1,148
2,012
3,192
2,269
4,527
11,314
15,679
7,443
25,079
24,486
50,802
80,019
19,397
25,454
67,683
75,250
63,512
199,682
197,480
939,977
24,350
22,659
26,419
46,196
22,075
15,609
32,159
15,222
43,025
4,648
9,566
13,883
10,447
20,365
50,920
62,018
33,483
110,818
106,714
214,393
324,921
0
0
0
0
0
0
0
1,209,890
43
D
Year and Source of Provincial Transmigration
Data (Statistical Yearbooks and Pocketbooks)
Year
Source
1956
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1972
1973
1974
1975
1978
1979
1980
1981
1988
1989
1990
1991
1992
1993
1994
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Badan
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Pusat
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
Statistik
(1957)
(1958)
(1959)
(1960)
(1961)
(1962)
(1967)
(1967)
(1967)
(1969)
(1971)
(1971)
(1971)
(1971)
(1973)
(1975)
(1976)
(1976)
(1978)
(1980)
(1981)
(1982)
(1994)
(1994)
(1994)
(1994)
(1994)
(1994)
(1995)
Notes: No data was available for 1957,
1971, 1976-1977, 1982-1987.
44